System and method for prostate visualization and cancer detection

ABSTRACT

A method, system, and computer-readable medium for detecting a disease of a prostate. Exemplary embodiments of the present disclosure can include receiving an image dataset acquired with at least one acquisition mode; segmenting a region of interest including the prostate from the dataset; applying conformal mapping to map the region of interest to a canonical shape; generating a 3D visualization of the prostate using the canonically mapped dataset; and applying computer aided detection (CAD) to the canonically mapped volume to detect a region of disease of the organ.

CROSS-REFERENCE TO PRIOR APPLICATIONS

This application claims priority from U.S. Provisional Application Ser.No. 61/297,454, filed on Jan. 22, 2010, which is incorporated byreference herein in its entirety.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH

The invention was made with government support under grant numberR01EB7530 awarded by the National Institutes of Health and grant numberIIS0916235 awarded by the National Science Foundation. The governmenthas certain rights in the invention.

FIELD OF THE DISCLOSURE

The present disclosure relates to medical imaging, and more specificallyto imaging for the diagnosis of prostate cancer (CaP).

BACKGROUND

Prostate cancer (CaP) is the most commonly diagnosed cancer among malesin Europe, and is the second leading cause of cancer related mortalityfor this same group. Although it is such a common cancer, diagnosismethods remain primitive and inexact. Detection relies primarily on theuse of a simple blood test to check the level of prostate specificantigen (PSA) and on the digital rectal examination (DRE). If anelevated PSA level is found, or if a physical abnormality is felt by thephysician during a DRE, then biopsies will be performed. Though guidedby transrectal ultrasound (TRUS), these biopsies are inexact, and largenumbers are often necessary to try and retrieve a sample from acancerous area. More recently, it has been noted that magnetic resonanceimaging (MRI) can be used for the detection of CaP. Multiple MR imagesobtained with different settings are necessary for the detection of CaP.Most commonly used is a combination of T2-weighted and T1-weighted imagesequences.

T2-weighted images are generally used to locate regions suspected ofbeing cancerous, while T1-weighted images are used to discount falsepositives, primarily due to the presence of post-biopsy hemorrhage. Theuse of MR spectroscopic imaging (MRSI) has also been suggested. Furtherdetails on the medical background of using MR T2-weighted, T1-weighted,and MRSI images to detect CaP is described in further detail hereinbelow.

MRSI measures chemical spectra in large regions covering many voxels.For CaP detection, there are three chemicals of interest: choline,creatine, and citrate. Specifically, the ratios of choline to creatineand of choline plus creatine to citrate appear elevated in regionscontaining CaP. MRSI is not considered suitable for specificlocalization due to its coarse resolution, but can be useful for a broadoverview of regions.

The acquisition of prostate MR image sequences is often done withvarying orientations and resolutions per sequence. In cases where theimage sequences are acquired during a single session, and withoutpatient movement, the resulting volumes will be naturally registered inworld space. Using the image position, orientation, and resolutioninformation of each MRI slice, the volumes can be oriented properly in3D space without the need for registration methods. Radiologists willtypically examine this data by simply viewing the 2D slices, and tryingto correlate matching positions between scans in various orientations(e.g., axial and coronal). However, this process is unintuitive andinefficient. A 3D rendering system, which would allow the physician toview the entire gland at once with the visualization including the datafrom each scan, would be more intuitive and efficient.

The surrounding anatomy can also be important in identifying CaP.Located superior to the prostate are seminal vesicles (SV), the invasionof which by CaP can also be of concern. Invasion of the SVs can beidentified using the T2-weighted images. Normal SVs appear as regions ofincreased intensity surrounded by walls of decreased intensity. In SVinvasion, the SVs will appear with decreased intensity throughout. Anabnormal angle between the prostate and the rectum can also beindicative of a problem, and thus it is important to be able to view thelocation of the rectal wall.

Further, multi-modal visualization is well suited to volumetric medicalimaging data and growing in popularity due to the proliferation ofvarious 3D medical imaging acquisition devices. The main task formulti-modal rendering is deciding how the volume data should be mixed.Often, the multimodal rendering is used to combine two volumes where oneincludes structural data and the other includes functional data. In suchcases, the two volumes are generally considered separately, with thefunctional data being used to highlight areas of interest on thestructural data. For cases with two modes, a 2D transfer function can beutilized to map a pair of sample values to a specified output color.

Volume rendering using ray casting has become a standard technique, andits highly parallel nature lends it naturally to acceleration on thegraphics processing unit (GPU). For GPU accelerated multi-volumerendering, work has often focused on slice-based approaches, where theslices from multiple volumes can be simply interleaved during rendering.For rendering via ray casting, it is common to use depth peeling andperform the ray casting in multiple passes or to do only certainportions at one time. Methods where the ray casting occurs in a singlepass typically require the volume datasets to be preprocessed such thatthey are registered and re-sampled to a single grid. Methods have alsobeen developed which address the problem of memory management forrendering large volumes which cannot fit in memory. However, the problemof memory management is typically not a significant issue for prostaterendering, as the region of interest is small.

SUMMARY OF EXEMPLARY EMBODIMENTS

MR images can assist in the detection of CaP, although slice-basedviewing can be difficult. Embodiments of the present disclosure canprovide an exemplary method for volume rendering of prostate MR data inan easy and efficient manner, allowing for the user to easily observethe prostate and suspicious regions in 3D. Further, computer aideddetection (CAD) techniques can be applied to the rendered prostatevolume data to assist in the detection of CaP. The exemplary method canbe applicable when multiple datasets have been acquired during the sameimaging session, with no patient movement between acquisitions, allowingfor the data to be naturally registered in world space. To handle themulti-oriented and multi-resolution volumes, the exemplary method caninclude an exemplary multi-volume ray casting algorithm wherein the rayintegration is performed in a single pass. Although the exemplary methodis optimized for rendering the prostate, it can be applicable to othermulti-volume rendering scenarios.

Exemplary embodiments of the present disclosure can provide a method,apparatus, and computer readable medium to perform 3D rendering,allowing a physician to view the entire gland with visualizationincluding data from multiple scans using multi-volume ray casting withmulti-modal shading. First, the image information can be extracted fromthe raw Digital Imaging and Communications in Medicine (DICOM) slices.Segmentation of the prostate region and trimming can be performed on thevolume to remove extraneous data. After this, three boundary pre-passesthrough the volumes' geometric data can be performed. The results fromthese pre-passes can then be used to perform multi-volume ray casting ina single pass through the data. The shading during this ray casting passis preferably accomplished using a multi-modal shading scheme whichconsiders T2-weighted image data, T1-weighted image data, and MRSIspectral data. The output of this pass can be the final rendered image,which the user can optimize by adjusting threshold parameters to controlto the multi-modal shading or by modifying the view.

Embodiments of the present disclosure can also include a method ofclassification for multi-modal MR rendering of the prostate that takesinto account T2-weighted, T1-weighted, and MRSI volumes. Unlike manyother multi-modal rendering applications, the values from the modes areused in deciding how a region is to be shaded, rather than simply usingone functional mode to highlight something from a structural mode. Theexemplary classification can be formulated as an equation which can beefficiently computed. The exemplary multi-volume ray casting andmulti-modal classification methods can be implemented on a GPU andoptimized for such an architecture.

Embodiments of the present disclosure can also include a framework forthe visualization of the prostate, its surrounding anatomy, andindications for tumor and hemorrhage location within the gland. Toprovide for this visualization, an exemplary score volume for renderingthe multi-modal data can be provided. The score volume can be firstcreated for the gland and seminal vesicles which takes into accountthree T2-weighted datasets, a T1-weighted dataset, and an MRSI dataset.Based on thresholds, every voxel can be scored as to whether each MRmode indicates a point of interest. This score volume can be integratedinto a slice-based viewing approach, or applied for 3D visualization ofthe region.

The prostate, the score volume, and the surrounding anatomy can bevisualized in an interactive framework which allows the user to adjustthe content being viewed. Various view modes of the score volume arepossible so that the user can focus on the desired results. An aspect ofthe present disclosure can include a visibility persistence mode,allowing one score to remain visible when it would otherwise beoccluded. The volume rendering can use a single pass multi-volume raycaster which is accelerated on the GPU to provide interactiveperformance.

Whereas previous 3D visualizations of the prostate have focused ondisplaying its shape, exemplary embodiments allow the user to viewmultiple types of information for the interior of the gland. Thismulti-modal information can be viewed as desired by the user. The use ofa score volume for volume rendering can be generalizable to any CADapplication, as the exemplary method of determining the scores can beseparate from the rendering.

According to exemplary embodiments of the present disclosure, up to sixvalues can be considered at each sample point. A 6D transfer function toincorporate these values may be used, but can be difficult to design. Asan alternative to this approach, a formula into which the values can beplaced is described herein below. The resulting value from thecomputation of this formula can then be used to map the sample to color.

Further exemplary embodiments of the present disclosure can also storethe volume information in GPU memory and perform the ray casting withina single pass without the need to resample the volumes to a unifiedgrid, allowing each volume to retain its native local coordinate system,resolution, and unfiltered quality.

Yet another exemplary embodiment of the present disclosure can provide amethod for performing upsampling of prostate volumes based on ternarylabelmaps, where the volume is segmented into the peripheral zone (PZ)and the central zone (CZ), and non-prostate regions. This exemplaryupsampling can be based on using three orthogonal T2-weighted imagesequences (axial, sagittal, and coronal). The first part of thealgorithm upsamples each volume individually by interpolating labelmapslices as needed. Given these three upsampled volumes, the second partof the algorithm can combine them to create a composite upsampledvolume, which can give a representation of the prostate. This exemplarytechnique can be implemented in prostate visualization techniques tocreate accurate and visually pleasing volume rendered images.

An exemplary embodiment of the present disclosure can provide a methodfor detecting a disease of a prostate. The exemplary method can includereceiving an image dataset acquired with at least one acquisition mode;segmenting a region of interest including the prostate from the dataset;applying conformal mapping to map the region of interest to a canonicalshape; generating a 3D visualization of the prostate using thecanonically mapped dataset; and applying computer aided detection (CAD)to the canonically mapped volume to detect a region of disease of theorgan. The disease can include a cancer, and the dataset can include aplurality of datasets acquired with at least two different acquisitionmodes.

The exemplary method can also include registering the plurality ofdatasets and correlating the plurality of datasets, and the conformalmapping can include the use of texture analysis.

According to the exemplary method, the computer-aided arrangement caninclude an electronic biopsy.

Another exemplary embodiment of the present disclosure can provide amethod for volume rendering of an organ. The exemplary method caninclude receiving a plurality of datasets acquired with at least twoacquisition modes; segmenting the plurality of datasets to define aregion of interest; executing a multi-volume ray casting algorithm;performing multi-modal shading; processing the plurality of datasetsusing the boundary pre-passes and the multi-volume ray castingalgorithm; generating an image of the organ using the processedplurality of datasets; and detecting a disease of the organ using acomputer-aided arrangement. The plurality of datasets can include atleast one of a T2-weighted endorectal axial scan; a T2-weightedendorectal sagittal scan; a T2-weighted endorectal coronal scan; aT1-weighted pelvic axial scan; and a MRSI, and the segmenting caninclude manually segmenting at least a portion of the plurality ofdatasets. Further, the multi-volume ray casting algorithm can include asingle pass performing a ray casting via a single traversal or aplurality of boundary pre-passes configured to identify at least one ofa direction for each ray and a step size for each ray. The plurality ofboundary pre-passes can identify at least one of a starting position inworld space for each ray and a starting position in local space for eachray.

The exemplary method can further include upsampling at least a portionof the plurality of datasets to create an upsampled volume, andgenerating the image using the upsampled volume. The upsampling caninclude creating an interpolated slice between two neighboring slices,labeling at least some voxels of the interpolated slice, eroding atleast some voxels labeled as undetermined or uncertain.

The exemplary method can further include extracting the plurality ofdatasets and combining images to form a plurality of volumes. Extractingthe datasets can include aligning the volumes in a world space. Theexemplary method can further include scoring the volumes to facilitate adiagnosis of a disease. The exemplary method can also includeclassifying at least portions of the generated image as at least one ofcancerous or normal, which can also include scoring at least a portionof the processed dataset.

According to an exemplary embodiment, the organ can be a prostate andthe disease can be a cancer.

According to another exemplary embodiment, the exemplary method caninclude creating a score volume including at least one score, each scoreassociated with at least one of T2-weighted images, T1-weighted images,or MRSI images. Further, the image can be generated at least partiallybased on the score volume.

The exemplary method can further include processing the plurality ofdatasets into at least one 3-dimensional texture, and the 3-dimensionaltexture include a volume cuboid.

Another exemplary embodiment of the present disclosure can include asystem for multi-modal volume rendering of an organ. The exemplarysystem can include an imaging arrangement configured to acquire an imagedataset acquired with at least one acquisition mode; and a computingarrangement configured to segment a region of interest including theprostate from the dataset, apply conformal mapping to map the region ofinterest to a canonical shape, generate a 3D visualization of theprostate using the canonically mapped dataset, and apply computer aideddetection (CAD) to the canonically mapped volume to detect a region ofdisease of the organ.

Yet another exemplary embodiment of the present disclosure can provide anon-transitory computer readable medium including instructions thereonthat are accessible by a hardware processing arrangement, wherein, whenthe processing arrangement executes the instructions. The processingarrangement can be configured to receive an image dataset acquired withat least one acquisition mode; segment a region of interest includingthe prostate from the dataset; apply conformal mapping to map the regionof interest to a canonical shape; generate a 3D visualization of theprostate using the canonically mapped dataset; and apply computer aideddetection (CAD) to the canonically mapped volume to detect a region ofdisease of the organ.

BRIEF DESCRIPTION OF THE DRAWINGS

Further objects, features and advantages of the present disclosure willbecome apparent from the following detailed description taken inconjunction with the accompanying Figures showing illustrativeembodiments of the present disclosure, in which:

FIG. 1 is a block flow diagram of an exemplary method according toexemplary embodiments of the present disclosure;

FIG. 2 is a block flow diagram of an exemplary method according toexemplary embodiments of the present disclosure;

FIGS. 3( a)-(e) are images of exemplary sample slices from five imagesequences in a data set according to exemplary embodiments of thepresent disclosure;

FIGS. 4( a) and (b) are exemplary illustrations of four image volumesequences having different orientations according to exemplaryembodiments of the present disclosure;

FIGS. 5( a) and (b) are exemplary sample images before and after imagetrimming according to exemplary embodiments of the present disclosure;

FIG. 6 is an exemplary screen shot illustrating an interface screenaccording to an exemplary embodiment of the present disclosure;

FIGS. 7( a)-(c) are exemplary images showing the effect of alteredthreshold values obtained using exemplary embodiments of the presentdisclosure;

FIGS. 8( a)-(c) are exemplary sample slice images obtained using toexemplary embodiments of the present disclosure;

FIGS. 9( a)-(d) are exemplary ternary labelmap interpolation imagesaccording to exemplary embodiments of the present disclosure;

FIGS. 10( a)-(c) are exemplary images obtained using compositesegmentation upsampling according to exemplary embodiments of thepresent disclosure;

FIGS. 11( a)-(c) are exemplary images of integrating a score volumeaccording to exemplary embodiments of the present disclosure;

FIGS. 12( a)-(c) are exemplary images of renderings of individual scorevalues according to exemplary embodiments of the present disclosure;

FIGS. 13( a)-(c) are exemplary images of renderings of score values withvarious levels of transparency according to exemplary embodiments of thepresent disclosure;

FIG. 14 is an exemplary image of seminal vesicles indicating bilateralinvasion obtained using exemplary embodiments of the present disclosure;

FIGS. 15( a)-(c) are exemplary images of renderings of visibilitypersistence according to exemplary embodiments of the presentdisclosure;

FIG. 16 is an exemplary image of the viewing angle between the prostateand the rectum obtained using exemplary embodiments of the presentdisclosure;

FIGS. 17( a)-(d) are exemplary images showing different types ofrendering according to exemplary embodiments of the present disclosure;

FIG. 18 shows an exemplary block diagram of an exemplary embodiment of asystem according to the present disclosure;

FIG. 19 shows an exemplary flow diagram of an exemplary method accordingto exemplary embodiments of the present disclosure; and

FIGS. 20( a)-(c) show illustrations of an exemplary prostate featuredetection according to exemplary embodiments of the present disclosure.

Throughout the drawings, the same reference numerals and characters,unless otherwise stated, are used to denote like features, elements,components, or portions of the illustrated embodiments. Moreover, whilethe present disclosure will now be described in detail with reference tothe figures, it is done so in connection with the illustrativeembodiments and is not limited by the particular embodiments illustratedin the figures.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

The present disclosure relates to imaging and volume rendering oforgans, such as the prostate. The present methods generally employmulti-modal imaging in order to enhance performance. According toexemplary embodiments of the present disclosure, multi-modal image datamay be acquired by a single imaging device and can be used to obtainboth the anatomical information as well as the cancerous regions. Ratherthan relying on a single scan to identify the cancer, multi-modalrendering can also be used to not just combine two items together(cancer and anatomy), but to identify the suspicious regions.

As shown in FIG. 1, an exemplary embodiment of the present disclosurecan provide an exemplary method, apparatus, and computer readable mediumto perform segmentation, visualization, and computer-aided detection(CAD) of CaP. Exemplary embodiments of the present disclosure can alsoprovide registration and correlation of multi-modal data.

In an exemplary embodiment, image data, such as, e.g., DICOM slices, canbe extracted (102). Next, the data can undergo a segmentation process(104) to isolate the prostate volume from surrounding tissue. The datamay be manually segmented, automatically segmented, semi-automaticallysegmented, or some combination thereof. The segmentation 104 can, forexample, differentiate between prostate and non-prostate tissue, andalso between the PZ and CG. Optionally, when certain multi-modal data isemployed, it may be preferable for the data sets to be registered (106)and correlated (108). Multi-modal data can include image data acquiredwith different protocols, images taken at different times, and the like.The registered and correlated data set can be used, for example, forsubsequent 3D visualization and rendering and identification of CaP.

Various methods of data, image and volume set registration may besuitable for use in the present methods. As one illustrative example,registration can be performed using anatomical feature points. FIGS. 20(a)-(c) show exemplary images that can be used for prostate featuredetection. FIG. 20( a) shows the anatomical position of the prostate.FIG. 20( b) shows each feature point with the pre-defined index numberis highlighted. FIG. 20( c) shows an exemplary multi-view of a prostateMR image along three directions. The prostate, a gland like a walnut insize and shape, typically does not contain a complicated geometricstructure. The prostate gland, which typically surrounds the urethra, istypically located in front of the rectum, and just below the bladder.

For volumetric feature registration, it is preferable to match at leastthree anatomical features within the MRI images of different directionsto obtain an accurate and reliable registration result. A pair of glandscalled the seminal vesicles are typically tucked between the rectum andthe bladder, and attached to the prostate as shown in FIG. 20( a). Theurethra goes through prostate and joins with two seminal vesicles at theejaculatory ducts. Therefore, some distinctive anatomical structures,such as the prostatic capsule and seminal vesicle contours, dilatedglands, and ejaculatory ducts as represented in FIG. 20( b), can beapplied for the exemplary registration process between different scandirections of one dataset or between MR slices and histology maps. Thiscan also be used to register various sets of image data, such as MRI,CT, PET, SPECT and other image scan data, should such multi-modal imagedata be of interest.

MRI can provides images with excellent anatomical detail and soft tissuecontrast. T1, T2-weighted datasets along the axial, sagittal and coronalview as shown in FIG. 3( c) can be analyzed. On each MRI prostate viewdirection, the exact outline of prostate boundary can be traced and eachcorresponding feature point can be manually marked with the predefinedindex number. MRI sequences are displayed in a serial order. Typically,two kinds of feature point can be used, e.g.: three internal featurepoints from the ejaculatory ducts (dilated gland inside the prostate),which can be the intersection of urethra and two seminal vesicles; andfour surface feature points also from the extra information of urethraand seminal vesicles. Because the urethra goes through the entireprostate, two surface feature points can be the entrance and exit pointsof the urethra. Meanwhile, with respect to the fact that two seminalvesicles attach to the prostate and merge with urethra at theejaculatory ducts, another two surface points can be marked at theintersection between each seminal vesicle and prostate.

The exemplary method can further include conformal mapping of theprostate (110). For example, the surface of the prostate can be mappedto a surface of a canonical geometric shape, such as a hollow sphere orcuboid, or the prostate volume can be mapped to a solid sphere.Alternatively, since CaP is typically located in the PZ and near thesurface, conformal mapping of the prostate surface with some thicknessto a sphere with a thick shell may be preferred. The conformal map canalso aid in registration of the data. The use of “texture analysis” onthe voxels of the prostate volume can be used to code the mapped surfaceimage, such as by applying different colors to those voxels which havediffering likelihood of CaP. Clustering of the coded image, such as bygrouping regions having similar voxel values or colors, can be used inCAD processes to allow a user to quickly identify regions where CaP islikely.

Further, the data can be used to perform visualization of the prostate(112). The visualization can include multi-modal 3D rendering of theprostate, or could also be provided on the conformal map. This caninclude T1-weighted, T2-weighted, and MRSI data. Further, thevisualization can include translucent rendering views that canfacilitate “electronic biopsies.” For example, an exemplary electronicbiopsy technique can include rendering a translucent volume onto aspherical shell and applying a transfer function expressly designed tomap prostate tissue so that healthy tissue can be differentiated fromcancerous tissue. Additionally, CAD techniques, such as the “electronicbiopsy” or clustering algorithms, can be used for the diagnosis of CaP(114).

Other exemplary embodiments of the present disclosure can provide anexemplary method, apparatus, and computer readable medium to perform 3Drendering of the prostate gland with visualization including data frommultiple scans using multi-volume ray casting with multi-modal shading.Steps of the exemplary method for rendering the prostate system isshown, for example, in FIG. 2. First, the image information can beextracted from raw data, such as, e.g., raw DICOM slices, (process 202).Segmentation of the prostate region and trimming can be performed on theimage volume to remove extraneous data (process 204). After this,boundary pre-passes through the volumes' geometric data can be performedin process 206. For example, three boundary pre-passes may be used. Theresults from these pre-passes can then be used to perform multi-volumeray casting in a single pass through the data in process 208. Theshading during this ray casting pass is preferably accomplished using amulti-modal shading scheme which considers T2-weighted image data,T1-weighted image data, and MRSI spectral data. The output of this passcan be the final rendered image (210), which the user can optimize byadjusting threshold parameters to control the multi-modal shading or bymodifying the view (processes 212, 214, 216).

The present system can provide the user an indication of the suspiciouslocations in 3D space, allowing the user to quickly tell where suchregions are in the entire prostate volume without the need to scrollthrough several individual 2D slices. Rather than attempt to make avoxel-level determination, the current system can be used as a tool toassist the user in finding regions of voxels that are suspicious andguide them to those areas that warrant further inspection.

Medical Background

To understand further about the development of a 3D multimodalvisualization system to assist in the detection of CaP, a briefdescription of the zonal anatomy of the prostate and the relationship ofthe three MR modes utilized is described. Examples of the types ofimages produced by these modes are shown in FIG. 3. FIG. 3( a) shows anexemplary T2-weighted endorectal axial slice. FIG. 3( b) shows anexemplary T2-weighted endorectal sagittal slice. FIG. 3( c) shows anexemplary T2-weighted endorectal coronal slice. FIG. 3( d) shows aT1-weighted pelvic axial slice. FIG. 3( e) shows an MRSI slice.

The prostate is divided into three zones, referred to as the peripheralzone (PZ), transitional zone (TZ), and central zone (CZ). The TZ and CZare often considered together as a single region in contrast to the PZ,and as such are referred to as the central gland (CG). The PZ is thelargest of the three zones, accounting for approximately 70% of theprostate, while the TZ and CZ each account for approximately 25% and 5%,respectively. It is therefore unsurprising that the PZ is also the mostcommon location for CaP to occur, with approximately 70% of casesoriginating there. Being on the periphery of the prostate, cancer fromthis region is also more likely to quickly spread beyond the prostaticcapsule. The CG is considered of relatively low importance compared tothe PZ, and thus in the present disclosure the focus is on detecting CaPin the PZ.

T2-weighted images provide good image quality of the prostate gland,allowing for a differentiation between the PZ and CG. For normalprostatic tissue, the PZ will typically demonstrate high signalintensity in the T2-weighted images. In cancerous tissue, the PZ willgenerally demonstrate a decreased signal intensity. In the CG, however,normal tissue already typically demonstrates a heterogeneous low signalintensity. Cancerous regions there may be detectable as areas ofhomogeneous low signal intensity. However, embodiments of the presentdisclosure focus on detecting CaP in the PZ.

Unlike T2-weighted images, T1-weighted images are of low image qualitywith respect to the prostate and are therefore not generally used toidentify cancerous regions. Rather, the T1-weighted images are typicallyused to exclude regions which may still contain blood from earlierbiopsies. Such post-biopsy hemorrhages typically appear similar tocancer in the PZ in T2-weighted images (that is, having a reducedintensity). However, in T1-weighted images, such regions typically haveincreased intensity from regular prostate tissue, which is ofhomogeneous low intensity. Cancerous regions are generally not apparentin T1-weighted images, since they also appear as areas of low intensity.MRSI for CaP detection looks at two ratios of chemicals, that of cholineto creatine and that of choline plus creatine to citrate. Both of theseratios typically appear elevated in CaP. In MRSI, these chemical spectracan be read in large voxel regions, which are not to be confused withhow the regular MR images are considered as voxels for volume rendering.Although usually aligned with the T2-weighted endorectal axial images,MRSI voxels are significantly larger, covering many normal image voxelsper slice. An example of the MRSI voxel size can be seen in FIG. 3( e),where the MRSI voxels for the slice are represented by a grid overlay onthe T2-weighted image.

Exemplary Data Pre-Processing

The data used can be raw DICOM files. According to an exemplaryembodiment of the present disclosure, a standard dataset can be used.For example, a standard dataset can be defined as a dataset which caninclude the following five image sequences:

1. T2-weighted endorectal axial scan;

2. T2-weighted endorectal sagittal scan;

3. T2-weighted endorectal coronal scan;

4. T1-weighted pelvic axial scan; and

For the T2-weighted image sequences, the data can be acquired, forexample, with approximately 0.5 mm intraslice and 3 mm intersliceresolutions. The T1-weighted images can be acquired at a much coarserresolution, for example, approximately 1 mm intraslice and 6 mminterslice. Examples of each of these image sequences can be seen inFIG. 3.

An exemplary method according to an exemplary embodiment of the presentdisclosure is described in further detail below.

Exemplary DICOM Extraction

As shown in FIG. 2, an exemplary method according to the presentdisclosure can include data extraction (202). Individual MR slices canbe delivered using the DICOM standard, a common format used by medicalimaging devices. From these raw DICOM files, images belonging to thesame scan can be combined in sequence to form volumes. For each volume,the image position (center of the upper left pixel) of the first slicecan be retained from the DICOM header information, as is the image pixel(x- and y-) resolution and orientation. The z-direction resolution canbe provided from the slice spacing information, and the z-orientationcan be calculated using the image position information from two slices.Using this extracted position, orientation, and resolution information,the volumes can be aligned with each other in world space, negating theneed to perform registration on the volumes. In other embodiments of thepresent disclosure, the image sequences may not be acquired during asingle session, or the patient may have moved during the sequences.Accordingly, it may be desirable for these images to be registered inaccordance with various registration processes. The orientation relationof the four image volumes can be seen, for example, in FIG. 4. FIG. 4(a) shows an exemplary four image sequence for a data set havingdifferent positions, orientations, and resolutions in world space toillustrate each volume extent. FIG. 4( b) shows an exemplary four imagesequence for a data set having different positions, orientations, andresolutions in world space to illustrate each center slice.

The T2-weighted and T1-weighted volumes can be straightforward tohandle, as they are conventional image data. The MRSI sequence, however,requires some more processing. As shown in FIG. 3( e), the MRSI imagescan be in a format easily readable by humans, but not in a form ready tobe used as a volume for rendering. As noted above, the two ratios ofinterest in MRSI (e.g., the ratio of choline to creatine and the ratioof choline plus creatine to citrate), can already be calculated andprovided above the spectras for each MRSI voxel. These ratio values canbe extracted. This extracted volume can then be used in the volumerendering flow shown in FIG. 2.

Exemplary Segmentation

As shown in FIG. 2, the exemplary method can include a segmentationoperation (204). For example, manual segmentation can be performed onthe T2-weighted axial slices. Although automatic and semi-automaticmethods for segmentation of the prostate can be used, since exemplaryembodiments of the present disclosure focus primarily on detecting CaPin the PZ, preferably, the PZ and CG are manually segmented as twoseparate regions. Since this T2-weighted endorectal axial volume ispreferably aligned in world space with the other volumes, othersegmentations are generally not necessary. Using the segmentationinformation, the volumes can be trimmed to include the segmented regionof interest. The alignment information between the volumes facilitatesthis to be accomplished on the volumes with a single segmentation. Aboundary of approximately 7 mm around the segmented region can beretained to provide some context in case the slices are viewedindividually.

This trimming operation can reduce memory requirements and results inincreased speed of ray casting because of less non-prostatic space toskip. FIG. 5 shows an example of the size difference between one sliceat the original size, with dimensions of 256×256 for a total of 65,536pixels (FIG. 5( a)), and at the trimmed size, with dimensions of 116×71for a total of 8,236 pixels (FIG. 5( b)). The total number of voxelspresent in the exemplary volumes containing these slices was 1,441,792voxels for the original size volume and 148,248 voxels for the trimmedvolume.

Exemplary Multi-Volume Ray Casting

As shown in FIG. 2, an exemplary method according to the presentdisclosure can include s multi-volume ray casting algorithm (e.g., 206and 208). The multi-volume ray casting algorithm preferably includesthree pre-passes through the scene boundary information, with raycasting performed in a single final pass. Each volume can be traversedby a ray which has its coordinate system local to that volume, but thetraversal preferably remains in step with the volumes in the worldcoordinate system (e.g., a step in one volume is equivalent in worlddistance to a step in another volume, although the steps within eachvolume's local system can be different). The three boundary pre-passesfacilitate setting the ray directions and step sizes, while the fourthpass can perform the ray casting with a single traversal through thevolumes.

Exemplary Boundary Pre-Passes

As shown in FIG. 2, an exemplary method according to the presentdisclosure can include a boundary pre-pass (206). In a certain example,three pre-passes through the geometric boundary data can be used toobtain the positional, directional, and stepping information along eachray for each volume. These passes can be done as pre-processing, and canbe repeated when the view or volume location in world space changes.Changes in a transfer function or other shading parameters can beperformed with a single ray casting pass. The first pre-pass, e.g., abounding front pass, can identify the starting position in world spacefor each ray. For each pixel in the image plane, this position can bethe volume position (considering the volumes being rendered) which isclosest to the image plane along the ray through the pixel. The outputsfrom this pass (the world starting position for each ray) can be used inthe third pass. The second pre-pass, e.g., a per-volume front pass, canidentify the starting position in world and local space for eachindividual volume along each ray. Similarly to the bounding front pass,for each pixel in the image plane, the closest position in each volumeto the image plane along the ray through the pixel can be calculated.The outputs from this pass can be, for each pixel, the local and worldentry positions for each volume. These can be used in the third pass.The third and final pre-pass, e.g., a per-volume back pass, can identifythe starting position and ray direction for each ray in local space, aswell as the number of steps from the starting position until the volumeis entered and the number of steps from the starting position until thevolume is exited.

The third pass can use the outputs from the two previous passes(bounding front pass and per-volume front pass). For this third pass,the furthest position along each ray for each volume can be calculated,and used together with the closest position information from theprevious pass to obtain the ray direction in local space. Using theinformation from both previous passes, the distance in local and worldspace from the boundary starting position to the beginning and end ofeach volume can be calculated. Using this distance information alongwith the calculated ray directions, the ray starting position in localspace can be calculated such that each ray will start at the samelocation in world space, although it might be outside of itscorresponding volume. The ray direction can be multiplied by the ratioof the distance in local space to the distance in world space in orderto ensure that a step along each ray is the same in world space. Thenumber of steps along the ray until the volume is entered and until thevolume is exited can then be calculated.

Exemplary Ray Casting Pass

An exemplary method according to the present disclosure can include aray casting pass (208). From the output of the final pre-pass, for eachvolume, every ray for every pixel in the image plane preferably has astarting position in local space, a ray direction in local space, thenumber of steps until the volume is entered, and the number of stepsuntil the volume is exited. Since the ray start positions and steps arepreferably calibrated, the rays remain at consistent positions in worldspace at each step, and thus the sample positions along each ray at eachstep remain consistent in the world coordinate system. Although it ispossible to step along the rays in the world coordinate system, thattypically requires a costly conversion at each step to each volume'slocal coordinate system. By stepping in the local coordinate systems tobegin with, this costly operation can be avoided. Since each ray is notinside of its volume the entire time from the ray starting point untiltermination, it is preferable to check whether or not this property istrue before attempting to sample the volume. Since the information forthe number of steps until the volume is entered and the number of stepsuntil the volume is exited is known, at each iteration the number ofsteps traversed can be checked to confirm it is within these two bounds.If so, the corresponding volume can be sampled. This check is preferablydone for every volume's ray. Since the volumes can be sampled separatelyat each step, their values can be integrated and operated on to providethe desired result.

For lighting of the rendered volume, since each volume can be traversedin its local coordinate system, the light position and eye position ispreferably in the corresponding local coordinate system for each volume.To obtain this position, the light and eye coordinates in the worldcoordinate system can be first rotated by the inverse of the scenerotation which is currently being applied to the volumes. Calculatingthe basic proportion between the distance from edge to edge for eachvolume in both local and world coordinate space and then from volumeedge to light or eye position in world coordinate space, it is possibleto solve for the light or eye position in local coordinate space.

Exemplary GPU Acceleration and Rendering

An exemplary method according to the present disclosure can include GPUacceleration and rendering (210). The exemplary framework formulti-volume ray casting can be readily mapped to the GPU foracceleration. The volume data values can be stored in 3D textures, andthus references to world space refer to the volume's physical positionin the 3D scene, while its local space is with regards to the 3D texturecoordinate system. In order to properly render the cuboid during passeswhich require front face culling, the direction of the vertices on thefront and back faces can be checked on loading and ensure they areconsistent for the datasets (counter-clockwise). For each volume, itseight bounding vertices can be used to construct the six quads whichcompose the volume cuboid. In an exemplary embodiment, unboundedfloating point textures can be used, facilitating the values to remainunsealed (not bound to the [0, 1] range). Preferably, multiple rendertargets can be used so that the multiple outputs required from somepasses can be output at once. The texture outputs can be created to bethe size of the render window, representing the final render imageplane. For values where the outputs are per-volume, a texture output foreach volume can be created.

An exemplary method of mapping each pass to a GPU is described in detailbelow:

Exemplary Bounding Front Pass: The volume boxes can be rendered withdepth testing. For each fragment nearest the virtual camera, itsposition in world space can be stored in the RGB channels of the outputtexture.Exemplary Per-Volume Front Pass: The fronts of each volume box can berendered individually. For each fragment, its position in world spaceand its position in its local texture coordinate system can be stored inthe RGB channels of two output textures (per volume).Exemplary Per-Volume Back Pass: Each volume box can be renderedindividually with front face culling on. The ray direction and raystarting position (in local texture space) for each volume can becalculated using the corresponding outputs from the previous passes.These results can be stored in the RGB channels of two output textures.The values for the number of steps to entry and steps to exit from thevolume extent can be calculated and stored in the alpha channels of thetwo output textures (per volume).Exemplary Ray Casting Pass: A single viewport-filling quad can berendered and the information for the ray casting can be obtained fromthe two output textures (per volume) obtained in the previous pass.Information regarding the positions of the lights and eye forillumination effects can be passed as uniform parameters for each volume(these values do not change per-volume on a fragment by fragment basis).

Exemplary Optimizations for Prostate Visualization

Aspects of the present disclosure can include optimization for prostatevisualization. The exemplary algorithm for multi-volume ray castingdescribed above has been described for general situations, where theregions to be sampled are not necessarily overlapping. However, for theprostate, the segmented region of interest is typically of moreinterest, which is present in each volume, accordingly, aspects of thepresent disclosure include some slight simplifications can be made tothe exemplary algorithm. For example, for prostate multi-volumerendering, sampling through the following six volumes can be performed,which can include:

1. T2-weighted endorectal axial image data;

2. T2-weighted endorectal sagittal image data;

3. T2-weighted endorectal coronal image data;

4. T1-weighted pelvic axial image data;

5. MRSI calculated ratios; and

6. segmentation of the PZ and CG.

However, since the MRSI values and segmentation information can both beincluded in volumes with the same settings as the T2-weighted axialimage data, four volumes can be processed by the pre-passes. Whenperforming the ray casting, since the segmented region may be of moreinterest, and the volume including this information may have the samelocal coordinate system as the T2-weighted axial volume, the positionson each ray can be jumped by the number of steps until the T2-weightedaxial volume is entered. Also, since the segmented region will generallybe present in the volumes, there is no need to check at each stepwhether the ray position is currently located inside of each volume.Once the segmented region is reached, the volumes can be sampled untilthe segmented region is exited. Once the number of steps taken by therays has passed the number needed to exit the T2-weighted axial volume,the casting for the rays emitted from the same pixel can be ended.

Exemplary Multi-Modal Shading

An exemplary method according to the present disclosure can also includemulti-modal shading in process 208. In one example, to calculate theshading at each step along the rays, six values from the five volumes inthe dataset (that is, intensity values from the three T2-weightedvolumes and one T1-weighted volume, as well as both ratios from the MRSIvolume) can be considered. The exemplary shading process can be used touse shading to indicate portions as cancerous or normal. Decidingwhether a sample should be labeled as cancerous or normal can be thoughtof as a group of exemplary if statements. For example, the exemplarystatements can include “If the ratio of choline to creatine is abovesome threshold, or if the ratio of choline plus creatine to citrate isabove some level, or if one of the T2-weighted images shows decreasedintensity (and if the T1-weighted image does not show an increasedintensity for that region), then that region is likely to be cancerous.”However, such a coarse classification tends to be unsuitable. First,selecting simply cancer or not for each region can be prone to error,and lacks any gradation from one result to the other. Another problemcan be that such a large number of dynamic branches performs very poorlyon the SIMD architecture of the GPU. In contrast, exemplary embodimentsof the present disclosure map the ray casting algorithm to the GPU toharness its superior processing power.

To overcome these limitations, each sample can be scored, and this scorethen mapped to color which contributes to the integration of valuesalong the ray. The exemplary formula can be as follows:

Score=MRSIA+MRSIB+T2A+T2S+T2C+T1A,

where, in one embodiment, the variable can be defined as:

MRSIA=(ratioA−threshMRSI)×percentage×0.5

MRSIB=(ratioB−threshMRSI)×percentage×0.5

T2A=(threshT2−T2axial)×0.333

T2S=(threshT2−T2sagittal)×0.333

T2C=(threshT2−T2coronal)×0.333

T1A=threshT1−T1axial

The values ratioA, ratioB, T2axial, T2sagittal, T2coronal, and T1axialcan be the sample values at the current position from the MRSI (ratios Aand B), T2-weighted axial, T2-weighted sagittal, T2-weighted coronal,and T1-weighted axial volumes, respectively. The threshold values can beoriginally set to a default value, but can be modified by the user toaccount for variances in the acquisition parameters of the MR data. TheMRSI threshold can be adjusted within the range of [0.0-4.0]. TheT2-weighted and T1-weighted images can be windowed to the range of[0.0-1.0], and thus their thresholds can be adjusted in the range of[0.0-1.0]. The higher the score from this formula, the more likely itmay be for the sample position to be from a cancerous location. For thevolume values, a threshold can be used to classify whether a value isconsidered cancerous or not. The distance from this threshold can beproportional to the likelihood there is that the sample is cancerous.

For MRSI, since elevated ratios indicate cancer, the threshold can belower. The opposite can be true for T2-weighted images, where a valuelower than the threshold indicates possible malignancy. Since the valuefrom the T1-weighted image is not typically used to detect cancer butrather to discount areas based on a high value, values less than thethreshold (in general, neutral) may be of interest. For the MRSI andT2-weighted values, the scores for those individual sections can beweighted so that the total summation of the parts from the same modalitycan be 1. The percentage of MRSI voxel including prostatic tissue can beused so that MRSI voxels mainly outside the prostate do not have as muchinfluence. This can be also used to control for locations where thereare no MRSI values, which would otherwise automatically give a negativecontribution to the score.

Alternatively, embodiments of the present disclosure can also includeother scoring concepts. For example, embodiments of the presentdisclosure can provide the concept of a score volume for visualizing thedisease and present methods to observe all three types of multi-modal MRdata in a single 3D view. User-driven rendering allows for differentinformation to be emphasized based on the user's desires. To this end,an exemplary method of visibility persistence, where a score of interestcan automatically maintain visibility when it would be occluded by otherscores, while the other scores maintain their normal opacity if they arenot occluding the score of interest. To handle rendering in thesurrounding prostate anatomy, a single pass multi-volume ray casteraccelerated on the GPU can be used. The score volume can also beintegrated into a 2D slice-based system.

The exemplary embodiment can include creating a score volume. In oneexample of a score volume, every voxel includes three values which canbe scores corresponding to each of the three types of MR acquisitions.Because a single score volume using all three orthogonal T2-weightedvolumes is created, it is preferable to first create an upsampled labelmap for each T2-weighted volume that is close to isotropic. In general,methods can use iterative dilations and erosions to interpolate middleslices throughout the volume, maintaining both individual segmentations(e.g., PZ and CG), as well as the area of the gland. This interpolationcan be repeated until the interslice spacing is no worse than twice theintraslice spacing. The three upsampled label maps can then be combinedto form a composite label map, which takes into account the segmentationinformation from all three T2-weighted volumes, and has an interslicespacing of 0.75 mm. The label map for the T1-weighted image sequence canbe likewise upsampled, yielding an interslice spacing of 1.5 mm.

Embodiments of the present disclosure can provide exemplary scorevolumes that include three score values: a T2 score based on theT2-weighted images, a T1 score based on the T1-weighted images, and anMRSI score. The T1 and MRSI scores can be binary, while the T2 score canbe quaternary. The inputs for the creation of the score volume caninclude five image sequences (e.g., T2-weighted axial prostate scan;T2-weighted sagittal prostate scan; T2-weighted coronal prostate scan;T1-weighted axial pelvic scan; and MRSI axial prostate scan), fourupsampled segmentation label maps, and a composite label map.

The exemplary score volume can be created, matching the dimensions andresolution of the composite label map volume, for the prostate regionbased on the three available MR modes. Scores can be generatedseparately for each of the three modes: a T2 score based on detectingcancer from the T2-weighted data; a T1 score based on detecting regionsof post-biopsy hemorrhage from the T1-weighted data; and an MRSI scorebased on detecting areas of increased chemical ratios indicating thepossibility of cancer occurring in a region from the MRSI data.

Empirically determined thresholds can be used to decide a score for eachof the modes. These thresholds can be defined by using a group of threedatasets for training and observing the typical signal intensities fornormal and abnormal regions in the PZ (decreased for T2, increased forT1, elevated spectra in MRSI). Pathology results can be used to ensurethat sampling from sextants known to contain either cancer or hemorrhagewas performed. Exemplary scores can be created as follows, with thedefault values being zero.

T2 Score (PZ): Decreased T2-weighted image intensity in the PZ can beindicative of cancer, and thus the voxels which are below a T2 thresholdmay be of interest. Since three volumes of T2-weighted data can be used,all of them can be sampled to take advantage of each volume's highintraslice resolution. Each volume's score can contribute one thirdtowards the final score.

T1 Score (PZ): Increased T1-weighted image intensity in the prostate canbe indicative of post-biopsy hemorrhage, and thus the voxels which areabove a T1 threshold may be of interest. The single T1-weighted volumecan contribute to the final score value.

MRSI Score (PZ and CG): An increase in one or both of the spectroscopicratios in the MRSI data can be indicative of prostate cancer. If eitherof the two ratios are above the MRSI threshold, then the voxel can bescored as being of interest. This scoring system, unlike for the T2 andT1 scores, can be applied to both the PZ and CG.

T2 Score (SVs): Similar to the T2 scoring for the PZ, decreasedT2-weighted image intensity in the SVs can be indicative of cancer.However, the SVs pose a hurdle in that their walls (both interior andexterior) also can appear with decreased T2-weighted intensity. Toaccount for this, a three part scoring process can be used. First, eachT2-weighted image sequence (axial, sagittal, and coronal) can be scoredindividually. Their individual score volumes can then be eroded by asmall number of voxels, e.g., two voxels, to remove thin boundaries. Thefinal SV score can be then created with each of the individual scorescontributing one third to the final score.

The neighboring regions of the PZ (prostatic capsule and CG) can begenerally dark, and thus could yield false positive results if includedaccidentally as part of the PZ. To account for this, the border voxelsare preferably not scored. To ensure that the sampling is from thecorrect region for each of the three T2-weighted volumes, the upsampledlabel maps for each volume, and preferably sample that volume only ifits label map indicates the region is correct. Likewise, the upsampledlabel map of the T1-weighted volume can also be provided to ensurevalues are not from outside the prostate when this volume is sampled.Since areas immediately outside of the prostate are often of increasedintensity in T1-weighted data, they could be mistaken as indicators of ahemorrhage if improperly sampled. Trilinear interpolation can be usedwhen sampling from the upsampled label maps and tricubic interpolationcan be used when sampling from the original MR datasets.

Exemplary Slice-Based Visualization

The exemplary created score volume can be integrated into a 2Dslice-based viewing system to provide guidance for the radiologist inviewing the slices by presenting information from other slices on theslice being viewed. For each voxel in a slice being viewed, the scorevalues from the score volume can be found and overlaid on the grayscaleimage. Though the score volume can be aligned with the axial T2-weightedimage sequence, it can be interpolated to obtain values for thecorresponding pixels in the other image sequences. Examples of this areshown in FIG. 11, where the T2 and T1 scores are shown in darker shading(1102) and lighter shading (1104) overlays, respectively. The user canadjust the opacity of the overlays as desired.

Exemplary Visualization

A 3D volume rendered view of medical imagery can be an intuitive methodof visualizing the data and obtaining a good sense of the relationshipbetween objects. In an exemplary embodiment, the user can visualize theprostate region (prostate gland and seminal vesicles) and thesurrounding anatomy in the pelvic region (bladder, rectum, and bone).For the prostate region, using the score volume allows the user tovisualize tumor and hemorrhage locations. The inputs for the volumerendering framework are the following four volume files:

1. Score volume2. Composite label map volume3. Upsampled label map of T1-weighted pelvic volume4. T1-weighted pelvic MR volume

The prostate region volumes (composite label map and score) can occupythe same volumetric space. Likewise, the pelvic region volumes(upsampled label map and MR values) can occupy the same volumetricspace. For rendering the surrounding anatomy, especially the bones, itis preferable to make use of the pelvic region volumes, which encompassa much greater area than the prostate region volumes. Since it ispreferable not to scale the prostate data up to the same size of thispelvic volume, it is preferable to perform multi-volume renderingthrough these two volumetric spaces. The score and label map volumes canbe preprocessed before being taken as input to the rendering framework.The prostate region volumes can be both trimmed so that much of thesurrounding area is removed where there is no prostate or SVs labeled.This trimming can be done such that a 3 mm border remains around thecuboid region of interest and will typically reduce its size to 15% ofthe original. Since the data has been based on binary segmentations withno smooth gradients between labeled and non-labeled regions, the scorevolume and both label map volumes can be filtered with a 3×3×3 meanfilter to improve the rendering results.

Exemplary Prostate Region

The exemplary visualization of the prostate region can be based on usingthe composite label map volume and the score volume. For rendering theinterior areas of the gland and SVs, the volume rendering can beperformed on the score volume. The score volume can include three valuesper voxel, corresponding to the T2-weighted score (indicating cancer inthe PZ), T1-weighted score (indicating hemorrhage in the PZ), and MRSIscore (indicating cancer anywhere in the gland containing spectroscopicvoxels). The user can view each of the values individually, or combinedas desired. For the surface of the gland, semi-transparent isosurfacerendering of the composite label map can be used directly.

An exemplary color scheme for the score values can also be used. Forexample, a high T1 score, indicating hemorrhage, can be shown in red.For regions with a high T2 score, blue can be used to represent thelocation of suspect cancerous areas. For the MRSI score, purple can beused to indicate increased ratios. The prostate gland itself can berendered as a semitransparent tan color and the seminal vesicles as asemitransparent green color. The transfer functions controlling thegland colors (prostate and SVs) can be applied to the label map volume,while the transfer functions for the score colors can be applied to thescore volume. The T2-weighted data itself is not used in the volumerendering.

The user can be presented with two standard options for rendering theprostate score data, for example:

Isosurface Score View: The solid isosurfaces of each of the score valuescan be viewed. This mode is typically done with a single score value ata time. Examples of the three scores rendered individually can be seenin FIG. 12. FIG. 12( a) is an exemplary rendering of T2 score valueswith the darker shading indicating cancer. FIG. 12( b) is an exemplaryrendering of T1 score values with the darker shading indicatinghemorrhages. FIG. 12( c) is an exemplary rendering of MRSI score valueswith the darker shading indicating elevated ratios. Since the T2 scorecan be a quaternary value, the isosurface can be set so that a score≧0.66 is preferred (i.e., at least two of the T2-weighted volumesindicated decreased signal).Transparent Score View: When viewing multiple scores together,user-defined transparency per score is typically used. This can beuseful if the user wants to see relationships and observe overlapsbetween different scores (e.g., between a cancerous T2 score and ahemorrhage T1 score). Examples of combinations of multiple scorerenderings with transparency are shown in FIG. 13. FIGS. 13( a)-(c) areexemplary renderings of FIGS. 12( a)-(c) shown with various levels oftransparency.

The seminal vesicles can be rendered along with the prostate gland.Since the only score within the seminal vesicles is the T2 score, itscoloring can be tied to that of the T2 score for the prostate gland andcan use the same blue color. Preferably, the user can maintain separatetransparency control over the seminal vesicles. A close-up example ofthe seminal vesicles with SV invasion indicated are shown in FIG. 14.

In addition to standard rendering of the prostate score volumes notedabove, a score rendering called visibility persistence can be provided.This mode can assist in keeping a score of interest (i.e., thepersistent score) visible when other scores may occlude it. For this, asecond volume rendering integral can be accumulated with reduced colorand opacity values for the non-persistent scores. The discretized volumerendering integral can then include the standard front-to-backcompositing as such:

C _(dst) ←C _(src)×(1−α_(dst))+C _(dst)

α_(dst)←α_(src)×(1−α_(dst))+α_(dst)

where

C _(src) ←C _(gland) +C _(PersistentScore) +C _(OtherScores)

α_(src)←α_(gland)+α_(PersistentScore)+α_(OtherScores)

and can also include:

C _(dst2) ←C _(src2)×(1−α_(dst2))+C _(dst2)

α_(dst2)←α_(src2)×(1−α_(dst2))+α_(dst2)

α_(score)←α_(PersistentScore)×(1−α_(score))+α_(score)

where

C _(src2) ←C _(gland) +C _(PersistentScore)+0.1×C _(OtherScores)

α_(src2)←α_(gland)+α_(PersistentScore)+0.1×α_(OtherScores)

At the end of the volume rendering integral, the final output color andopacity can be composited as such:

C _(dst) ←C _(dst2)×α_(score) +C _(dst)×(1−α_(score))

α_(dst)←α_(dst2)×α_(score)+α_(dst)×(1−α_(score))

where the α_(score) value for blending can be used to prevent a jaggedhalo effect around the persistent score. As shown in FIG. 15, thespatial relationships of the scores and gland can be fully maintained bythis exemplary method. In contrast to making one score transparent toview another occluded behind it, this mode allows for an occluded scoreto automatically be visible, while the other scores can maintain theirfull opacity unless they are occluding the score of interest. FIG. 15(a) is an exemplary rendering of normal isosurface rendering of the T2(1502) and T1 (1504) scores. FIG. 15( b) is an exemplary rendering ofthe T2 score having visibility persistence. FIG. 15( c) is an exemplarytransparent rendering of the T1 score to allow viewing of the T2 score.In the exemplary rendering of FIG. 15( c), the T1 score isnon-transparent in regions where it is not occluding the T2 score.

Exemplary Surrounding Anatomy

When including the surrounding anatomy in the rendering, single-passmulti-volume rendering can be used. For each pixel in the renderedimage, the ray starting position and direction can be calculated forboth the prostate region volume and the pelvic region volume. The stepsalong each ray can be both adjusted to be the same step size, such thatstepping along one ray can be correlated with stepping along the otherray. The number of steps to enter and exit each of the volumes can becalculated. Since the pelvic region is typically larger and fullyencompasses the smaller prostate region, a sample position in theprostate region can also be within the pelvic region, though most samplepoints within the pelvic region will not be within the prostate region.Because of this, the number of steps inside the pelvic region before theray reaches the prostate region, the number of steps that it will be inboth, and the number of steps after the prostate region before exitingthe pelvic region can be calculated. Using these values, the rays can becast through the volumes, and the prostate region can be sampled whenthe current ray step position is within the correct range.

The pelvis and other nearby bones account for the majority of the areain the pelvic region volumes. When the bones are not being rendered, theminimum and maximum extent of the remaining anatomy (e.g., the bladderand rectum) can be calculated and the sampling rays can be cast throughthis bounding box, reducing the amount of area being traversed toapproximately 10% of the full size. Note that the prostate region can bebetween these two objects and thus can be included and will not bemissed.

The rectum (or more properly, the endorectal coil) can be rendered as asemi-transparent isosurface 1602. The user can be able to easily observethe angle between the rectum 1602 and the prostate surface 1604 (seeFIG. 16). Similar to the rectum, the bladder can be rendered as a yellowsemi-transparent isosurface. The bones can be rendered as slightlyoff-white isosurfaces. Unlike for the prostate region, the transferfunctions controlling the color and opacity values for the pelvic regioncan be applied to the T1-weighted MR data.

Exemplary Embodiment

An exemplary implementation of an embodiment according to the presentdisclosure can include the standard clinical protocol for MR imaging ofthe prostate, where the five MR sequences listed above can be acquiredfor each patient. The exemplary methods can be tested, for example, on asystem running on a Core 2 Quad QX9300 2.54 GHz CPU with 4 GB of RAM andan NVIDIA FX 3700M video card.

FIG. 18 shows an exemplary block diagram of an exemplary embodiment of asystem according to the present disclosure. For example, exemplaryprocedures in accordance with the present disclosure described hereincan be performed by or controlled using an MRI 1880, a hardwareprocessing arrangement and/or a computing arrangement 1810, and an inputdevice 1890. Such processing/computing arrangement 1810 can be, e.g.,entirely or a part of, or include, but not limited to, acomputer/processor 1820 that can include, e.g., one or moremicroprocessors, and use instructions stored on a computer-accessiblemedium (e.g., RAM, ROM, hard drive, or other storage device).Preferably, the processing arrangement 1810 includes a GPU which isoptimized for performing high speed graphics operations.

As shown in FIG. 18, e.g., a computer-accessible medium 1830 (e.g., asdescribed herein above, a storage device such as a hard disk, floppydisk, memory stick, CD-ROM, RAM, ROM, etc., or a collection thereof) canbe provided (e.g., in communication with the processing arrangement1810). The computer-accessible medium 1830 can contain executableinstructions 1840 thereon. In addition or alternatively, a storagearrangement 1850 can be provided separately from the computer-accessiblemedium 1830, which can provide the instructions to the processingarrangement 1810 so as to configure the processing arrangement toexecute certain exemplary procedures, processes and methods, asdescribed herein above, for example.

Further, the exemplary processing arrangement 1810 can be provided withor include an input/output arrangement 1870, which can include, e.g., awired network, a wireless network, the internet, an intranet, a datacollection probe, a sensor, etc. As shown in FIG. 18, the exemplaryprocessing arrangement 1810 can be in communication with an exemplarydisplay arrangement 1860, which, according to certain exemplaryembodiments of the present disclosure, can be a touch-screen configuredfor inputting information to the processing arrangement in addition tooutputting information from the processing arrangement, for example.Further, the exemplary display 1860 and/or a storage arrangement 1850can be used to display and/or store data in a user-accessible formatand/or user-readable format.

The initial virtual camera orientation for the volume rendering can beLPS (Left-Posterior-Superior) orientation, which is the standard DICOMorientation. Intuitive navigation around the scene can use arcballrotation based at the center of the prostate region. Ray casting can beperformed by a GPU. The label map volumes can be stored in RGBAtextures, where the alpha component indicates the existence of a valuein the RGB components. Each score value or segmentation label can bestored in its own channel, allowing utilization of the highly efficientlinear interpolation of the GPU when determining what object a samplepoint belongs to.

The step size for the ray casting depends on whether or not the user isalso viewing the surrounding anatomy. Since the pelvic region volumedata is much larger than the prostate region and is of half theresolution, a larger step size can be used to improve performance. Whenthe surrounding anatomy is included, a step size of 0.5 mm can be used.When only the prostate region is being rendered, a step size of 0.25 mmcan be used. The compositing of samples along the ray can be adjustedbased on the step size such that the view is consistent betweenrendering with and without the surrounding anatomy. Stochastic jitteringcan be used to reduce woodgrain artifacts. Early ray termination(α>0.95) can be used when rendering the surrounding anatomy.

The exemplary embodiment can include four basic computational processes.Given the four label map segmentations, they can be first upsampled. Thethree upsampled T2-weighted label maps can then be combined to form acomposite label map. This composite label map, along with the originalMR volumes, can then be used to create the score volume. Finally, thescore volume and composite label map volume can be trimmed and these twoalong with the upsampled T1-weighted label map are mean filtered toimprove the rendering results.

The performance of the volume rendering varies depending on what regionsand objects are being viewed. The renderings maintain interactiveperformance. FIG. 17 shows the four basic types of rendering that havediffering performance (all objects, without bone, prostate only, andpersistence), and their performance in frames per second (fps). Theseare rendered at an image size of 512×512 pixels.

Exemplary Evaluation

Although the current focus can be on developing the visualizationtechniques for a CaP detection system, the exemplary scoring system wascompared against the results of the ACRIN 6659 study. For this exemplarystudy, the MR acquisitions were performed on patients 4-6 weeks afterneedle biopsy and before radical prostatectomy. The determinations ofboth radiologists and pathologists were denoted for the six sextants ofthe prostate. Because the results from the MRSI can be very broad andnon-specific, the T2 and T1 scores were considered in the evaluation.

For each patient dataset, a total of eight radiologists would review theMR data and make determinations as to the presence of cancer andhemorrhage on a per-sextant basis. For the cancer determinations, aranking on a scale of one to five can be used, with one indicatingdefinitely no cancer and five indicating definitely cancer. For theexemplary comparison, the minimum and maximum rankings were not used,the remaining six can be averaged, and a ranking of three or greater canindicate cancer. For determining hemorrhage, the radiologists' resultscan be taken as the standard since this is not indicated from thepathology. For the determination of cancer, the radiologists' resultscan be used for comparison.

Given the excised prostate, a pathological analysis can be performed onit, and results can be reported for cancer in the prostate, again on aper-sextant basis. The pathology report can also indicate whether or notthere was invasion of the seminal vesicles. The results from thepathology (both cancer determination in the prostate and seminal vesicleinvasion) can be taken as the standard in evaluating the exemplarysystem.

The results of testing the exemplary system on three datasets aresummarized in Tables 1-3. For the determination of cancer in a sextant(Table 1), the results from the exemplary system were better than fromthe radiologists. The exemplary results for the exemplary method fordetecting SV invasion is shown in Table 2. For the detection ofhemorrhages (Table 3), the simple threshold method can be quiteefficient.

TABLE 1 Sextant evaluation for cancer in the prostate (pathology isground truth). Cancer Pathology T₂ Score Radiologists Diagnosis (#Sextants) (# Correct) (# Correct) cancer 15 14 7 no cancer 3 3 2

TABLE 2 Seminal vesicle evaluation for cancer invasion (pathology isground truth). Seminal Vesicle Pathology T₂ Score Invasion (# Cases) (#Correct) yes 2 1 no 1 0

TABLE 3 Sextant evaluation for hemorrhage (radiologists' determinationis ground truth). Post-Biopsy Radiologists T₁ Score Hemorrhage (#Sextants) (# Correct) hemorrhage 6 6 no hemorrhage 12 12

An exemplary screenshot of a graphical user interface (GUI) for theexemplary multi-volume multimodal rendering system (showing the detectedcancer—602) is illustrated in FIG. 6. Because the exact acquisitionparameters can vary between datasets, three slider bars 604, 606, 608can be provided on the right side of the interface which allow the userto interactively adjust the three threshold values. To reduce samplingartifacts in the rendering, a step size of 0.25 mm can be used inkeeping with the Nyquist-Shannon sampling theorem (the highestresolution in the data is approximately 0.5 mm).

The exemplary result of this exemplary rendering framework can be seenin FIG. 7( a). The pathology performed on the radical prostatectomyspecimen, for example, found a high score Gleason 7 cancer in both theleft and right mid-gland and base regions, which are highlighted as“suspicious” in the exemplary rendering. A false positive section at thetop of the apex is also observed.

FIGS. 7( b)-(c) show how modifying the thresholds affects the renderedimage. Because of the low resolution in the z-direction, the renderedview can take on a bit of a stepped appearance, where the boundarybetween slices can be seen. FIG. 7( b) shows an exemplary rendering ofincreasing the MRSI ratio threshold in less areas being shown assuspicious. FIG. 7( c) shows an exemplary rendering of increasing theT2-weighted threshold results in more areas being shown as suspicious.If a smoothed look is preferred, a smooth surface could be fit aroundthe segmentation. To increase the z-resolution, it might also bepossible to use exemplary techniques to insert slices, which could alsobe segmented to smooth the boundary between the PZ and CG. Althoughseveral volumes are preferably sampled at each point and a scorecalculated to obtain an index for the transfer function, the small sizeof the volumes and the efficient scoring equation, for example, allowfor the image to be rendered at interactive frame rates of approximately12 frames per second in a render view of 512×512 pixels. The coding forthe exemplary system can be written in C++ and can use OpenGL and Cgvertex and fragment shaders for the visualization. The exemplary methodscan be tested, for example, on a system running on a Core 2 Quad QX93002.54 GHz CPU with 4 GB of RAM and an NVIDIA FX 3700M video card.

Another exemplary embodiment of the present disclosure can provide amethod for upsampling prostrate segmentation labelmap slices prior tocombining multiple views into a single composite labelmap to produce asmoother and more realistic rendering. Preferably, the exemplary methodsincorporate ternary segmentation, and thus, an exemplary ternary shapebased segmentation interpolation method in which known regions can bedilated into unknown regions to form the final shape in an interpolatedslice can be provided. Preferably, information from multiple labelmapscan be used to create the final upsampled labelmap. The exemplary methodcan be fast, easy to implement, and suitable for CaP visualizationneeds.

Exemplary Upsampling Method

According to another exemplary embodiment of the present disclosure canprovide an exemplary method of upsampling. For example, threeT2-weighted image sequences, which are approximately orthogonal, can beused so that the final shape from the segmentations and upsampling canbe as accurate as possible. Specifically, the three scans used, e.g.,can be a T2-weighted endorectal axial scan, a T2-weighted endorectalcoronal scan, and a T2-weighted endorectal sagittal scan. A sample slicefrom each of these scans is shown in FIG. 8. FIG. 8( a) shows anexemplary T2-weighted endorectal axial slice. FIG. 8( b) shows anexemplary T2-weighted endorectal sagittal slice. FIG. 8( c) shows anexemplary T2-weighted endorectal coronal slice. The relationship betweenthe scans is shown in FIG. 4, with the axial slice (408), the sagittalslice (402), and the coronal slice (406). Also shown is a T1-weightedslice (404). For these T2-weighted image sequences, the data can beacquired with approximately 0.55 mm intra-slice and 3 mm inter-slicespacing. These scans can be acquired during a single session withoutpatient movement, and thus can be naturally aligned using their positionand orientation information.

The segmented volumes of three orientations of T2-weighted data (e.g.,axial, coronal and sagittal) can be the inputs to the exemplaryupsampling method. These segmentations are preferably in the form ofternary labelmaps. These labelmap volumes can include ternarysegmentation information, rather than simply a binary segmentation,because the zonal anatomy of the prostate can be taken into account.Each labeled voxel can be indicated as either not belonging to theprostate, belonging to the region of the PZ, or belonging to theremaining portion on the gland. This remaining portion can include boththe CG region and the fibromuscular stroma, however, this labeled regionwill be simply referred to as the CG region.

Using the image position, resolution, and orientation information fromthe DICOM data, the image volumes can be aligned properly in 3D spacewith respect to each other. An example of this accurate alignment of thefour image sequences of one dataset is shown in FIG. 4( b). Because ofthis alignment, registration is not necessary, and corresponding voxelpositions can be easily found between the three volumes. In otherembodiments of the present disclosure, the image sequences may beacquired in more than a single session, or the patient may have movedduring the sequences during a single imaging session. In these cases, itis generally preferable for these images to be registered in accordancewith various registration processes.

Exemplary Labelmap Upsampling

An exemplary upsampling method according to an exemplary embodiment ofthe present disclosure is shown in FIG. 19. The first part of theexemplary upsampling method can include upsampling each T2-weightedternary labelmap volume separately along its z-axis by interpolating newslices in order to reduce the inter-slice spacing to the level of theintra-slice spacing. The ternary segmentation can be encoded, forexample, into the voxels as follows: voxels not belonging to theprostate can be assigned a value of 0, voxels belonging to the PZ can beassigned a value of 10, and voxels belonging to the CG can be assigned avalue of 30. The result of the exemplary upsampling will likewiseinclude these three values when completed. Embodiments of the presentdisclosure can provide a simple method based on iterative erosions anddilations which will take this ternary data into account, preserving theshape of the entire gland as well as of the individual zonal regions.

An interpolated slice can be created midway between each pair ofneighboring slices in the original labelmap volume (1902). The exemplaryalgorithm can include four steps which are performed on the interpolatedslice that is to be created. These four steps can be repeated as neededto reduce the inter-slice spacing of the volume to the level of theintra-slice spacing. In the description below, the use of the termneighboring voxels refers to the two neighboring voxels from the twoneighboring slices. That is, given two slices A and B, for theinterpolated slice AB between A and B, a voxel vAB with position (x, y)in the interpolated slice can include two neighbor voxels vA and vB withposition (x, y) in slices A and B, respectively.

The first step in this exemplary algorithm can be an initial labeling ofthe voxels in the interpolated slice (block 1904). For the voxels vAB inthe interpolated slice, its value can be set to be the mean of the twoneighboring voxels, vA and vB. If both neighboring voxels are labeled asnon-prostate, then the corresponding interpolated voxel is likely alsonon-prostate and is correctly labeled 0. If both neighboring voxels areeither PZ or CG, then the corresponding interpolated voxel is likelyalso PZ or CG, and it is correctly labeled as 10 or 30, respectively. Ifone neighboring voxel is PZ and the other is CG, then the interpolatedvoxel will likely be in the prostate, but it is as yet undetermined asto whether it should be labeled as PZ or CG (its current value is set to20). If the interpolated voxel is between a prostate voxel and anon-prostate voxel, then it will be labeled as uncertain (value of 5 or15) and will be further processed.

The second step can be an erosion of the areas that have been labeled asuncertain (labeled as 5 or 15) (block 1906); that is, areas that couldbe inside or outside of the prostate. If an area is known to belong tothe prostate, it can be referred to as certain (note that voxels whichmust belong to the prostate but can be either PZ or CG are referred toas certain but undetermined). The uncertain regions can be eroded byperforming iterative dilations on the certain regions into the uncertainregions. After this step, the voxels in the interpolated slice can belabeled as one of the four certain types. Note that the undeterminedvoxels (labeled as 20) can also be dilated, such that they grow outwardsfrom their initial locations.

The third step can include re-labeling voxels as belonging to the PZ orCG (block 1908). For this step, a decision can be made for theundetermined voxels (labeled as 20). Since this region was grown duringthe previous step, some of these undetermined voxels, for example, maynow have a prostate label in one neighboring slice and a non-prostatelabel in the other neighboring slice. Since these voxels are likelyincluded within the prostate, they can be labeled with the PZ or CGlabel from its corresponding prostate neighbor (value 10 or 30).

The next step for the exemplary z-resolution upsampling can be a furthererosion of the remaining undetermined voxels (labeled as 20), whichbelong to the prostate but are not yet labeled as PZ or CG (block 1910).These voxels can be eroded similarly to the second step above, thoughpreferably, the PZ labels (value of 10) and CG labels (value of 20) areallowed to grow into them, as it is known that the voxel belongs to theprostate and thus the non-prostate voxels (value of 0) are preferablynot allowed to grow into them. After this step, the voxels in theprostate will preferably be labeled as belonging to either the PZ or CG.

After these four steps, the voxels are preferably labeled as eithernon-prostate (value of 0), PZ region (value of 10), or CG region (valueof 30), preserving the ternary state of the labelmap. This exemplarymethod is preferable over a conventional binary shape-basedinterpolation approach in order to avoid gaps. If each prostate region(PZ and CG) is interpolated separately, gaps can occur in the resultinginterpolated labelmap that should be covered by the prostate. An exampleof this problem is shown in FIGS. 9( a)-(d). FIGS. 9( a)-(d) showillustrations of exemplary ternary labelmap interpolation. In FIGS. 9(a)-(d), the PZ label is shown as 904 and the CG is labeled 902. FIGS. 9(a) and (b) show two neighboring slices from the original labelmapvolume. FIG. 9( c) shows an interpolated slice where the PZ and CGregions are interpolated separately, resulting in a large missing areawhere the prostate should be. FIG. 9( d) shows an exemplary result fromthe exemplary ternary method, where the shape of the overall prostatesegmentation has been preserved an no prostate area is missing.

Exemplary Composite Labelmap

The second part of the exemplary upsampling algorithm can includecreating a composite upsampled labelmap volume. The three upsampledlabelmap volumes from the T2-weighed data can be used in creating thiscomposite volume, capitalizing on the good intra-slice resolution of thegenerally orthogonal datasets. That is, the axial volume can be taken asthe canonical orientation for xyz, then it may have good resolution in xand y, but poor in z, and thus the segmentation might be off slightly inthat dimension. However, the coronal volume may have good resolution inx and z, while the sagittal volume may have good resolution in y and z.In this way, each dimension may be encompassed by the good intra-sliceresolution data from two volumes.

For this exemplary composite labelmap, the axial T2-weighted upsampledlabelmap can be used as the coordinate system. For each voxel in thecomposite volume, an average labelmap can be computed using the labelmapvalues from the three upsampled labelmaps. Areas where either two or allthree segmentations agree are preserved. That is, at least two of thethree upsampled labelmaps preferably agree that a voxel is in theprostate in order for it to be labeled as such, helping to removeoutliers. This composite labelmap can result in a more accurate andvisually pleasing representation of the prostatic volume.

Exemplary results of an exemplary implementation of the exemplary simpleprostate upsampling are shown for one dataset in FIG. 10. The voxelspacing from the original image sequences is 0.55×0.55×3 mm for each ofthe sequences (e.g., axial, sagittal, and coronal). For this resolutionof data, the upsampling interpolation can be performed twice for each ofthe three T2-weighted image volumes, yielding an inter-slice resolutionof 0.75 mm. FIG. 10( a) shows an isosurface rendering of the originalsegmented prostate from the T2-weighted axial image sequence. Due to thelarge inter-slice gap, obvious plateaus can be visible where each slicewas segmented. FIG. 10( b) shows a rendering of the exemplary resultfrom upsampling the axial sequence alone. The plateau artifacts havebeen greatly reduced, though are still somewhat disturbing. FIG. 10( c)shows a rendering of the final composite segmentation based on combiningthe upsampling of the axial, sagittal, and coronal sequences. Thefaceted artifacts have been further reduced, and the entire shape of theprostate can be more full and accurate due to the contributions to theshape from the sagittal and coronal views.

The foregoing merely illustrates the principles of the disclosure.Various modifications and alterations to the described embodiments willbe apparent to those skilled in the art in view of the teachings herein.It will thus be appreciated that those skilled in the art will be ableto devise numerous systems, arrangements, and procedures which, althoughnot explicitly shown or described herein, embody the principles of thedisclosure and can be thus within the spirit and scope of thedisclosure. In addition, all publications and references referred toabove can be incorporated herein by reference in their entireties. Itshould be understood that the exemplary procedures described herein canbe stored on any computer accessible medium, including a hard drive,RAM, ROM, removable disks, CD-ROM, memory sticks, etc., and executed bya processing arrangement and/or computing arrangement which can beand/or include a hardware processors, microprocessor, mini, macro,mainframe, etc., including a plurality and/or combination thereof. Inaddition, certain terms used in the present disclosure, including thespecification, drawings and claims thereof, can be used synonymously incertain instances, including, but not limited to, e.g., data andinformation. It should be understood that, while these words, and/orother words that can be synonymous to one another, can be usedsynonymously herein, that there can be instances when such words can beintended to not be used synonymously. Further, to the extent that theprior art knowledge has not been explicitly incorporated by referenceherein above, it can be explicitly being incorporated herein in itsentirety. All publications referenced can be incorporated herein byreference in their entireties.

1. A method for detecting a disease of a prostate, comprising: receivingan image dataset acquired with at least one acquisition mode; segmentinga region of interest including the prostate from the dataset; applyingconformal mapping to map the region of interest to a canonical shape;generating a 3D visualization of the prostate using the canonicallymapped dataset; and applying computer aided detection (CAD) to thecanonically mapped volume to detect a region of disease of the organ. 2.The method of claim 1, wherein the disease includes a cancer.
 3. Themethod of claim 1, wherein the dataset comprises a plurality of datasetsacquired with at least two different acquisition modes.
 4. The method ofclaim 1, further comprising registering the plurality of datasets. 5.The method of claim 1, further comprising correlating the plurality ofdatasets.
 6. The method of claim 1, wherein the computer-aidedarrangement includes an electronic biopsy.
 7. The method of claim 1, fwherein the conformal mapping further comprises the use of textureanalysis.
 8. A method for volume rendering of an organ, comprising:receiving a plurality of datasets acquired with at least two acquisitionmodes; segmenting the plurality of datasets to define a region ofinterest; executing a multi-volume ray casting algorithm; performingmulti-modal shading; processing the plurality of datasets using theboundary pre-passes and the multi-volume ray casting algorithm;generating an image of the organ using the processed plurality ofdatasets; and detecting a disease of the organ using a computer-aidedarrangement.
 9. The method of claim 8, wherein the plurality of datasetsincludes at least one of a T2-weighted endorectal axial scan; aT2-weighted endorectal sagittal scan; a T2-weighted endorectal coronalscan; a T1-weighted pelvic axial scan; and a MRSI.
 10. The method ofclaim 8, wherein segmenting includes manually segmenting at least aportion of the plurality of datasets.
 11. The method of claim 8, furthercomprising upsampling at least a portion of the plurality of datasets tocreate an upsampled volume, and the image is generated using theupsampled volume.
 12. The method of claim 8, wherein the multi-volumeray casting algorithm includes a plurality of boundary pre-passesconfigured to identify at least one of a direction for each ray and astep size for each ray.
 13. The method of claim 12, wherein theplurality of boundary pre-passes identifies at least one of a startingposition in world space for each ray and a starting position in localspace for each ray.
 14. The method of claim 8, wherein the multi-volumeray casting algorithm includes a single pass performing a ray castingvia a single traversal.
 15. The method of claim 8, further comprisingextracting the plurality of datasets and combining images to form aplurality of volumes.
 16. The method of claim 8, wherein extracting theplurality of datasets includes aligning the volumes in a world space.17. The method of claim 8, wherein the organ is a prostate.
 18. Themethod of claim 15, further comprising scoring the volumes to facilitatea diagnosis of a disease.
 19. The method of claim 16, wherein thedisease is cancer.
 20. The method of claim 11, wherein the upsamplingincludes creating an interpolated slice between two neighboring slices.21. The method of claim 20, wherein the upsampling includes labeling atleast some voxels of the interpolated slice.
 22. The method of claim 21,wherein the upsampling includes eroding at least some voxels labeled asundetermined or uncertain.
 23. The method of claim 8, further comprisingclassifying at least portions of the generated image as at least one ofcancerous or normal.
 24. The method of claim 23, wherein the classifyingincludes scoring at least a portion of the processed data set.
 25. Themethod of claim 8, further comprising creating a score volume includingat least one score, each score associated with at least one ofT2-weighted images, T1-weighted images, or MRSI images.
 26. The methodof claim 25, wherein the image is generated at least partially based onthe score volume.
 27. The method of claim 8, further comprisingprocessing the plurality of datasets into at least one 3-dimensionaltexture.
 28. The method of claim 27, wherein the 3-dimensional textureinclude a volume cuboid.
 29. A system for multi-modal volume renderingof an organ, comprising: an imaging arrangement configured to acquire animage dataset acquired with at least one acquisition mode; and acomputing arrangement configured to segment a region of interestincluding the prostate from the dataset, apply conformal mapping to mapthe region of interest to a canonical shape, generate a 3D visualizationof the prostate using the canonically mapped dataset, and apply computeraided detection (CAD) to the canonically mapped volume to detect aregion of disease of the organ.
 30. A non-transitory computer readablemedium including instructions thereon that are accessible by a hardwareprocessing arrangement, wherein, when the processing arrangementexecutes the instructions, the processing arrangement is configured to:receive an image dataset acquired with at least one acquisition mode;segment a region of interest including the prostate from the dataset;apply conformal mapping to map the region of interest to a canonicalshape; generate a 3D visualization of the prostate using the canonicallymapped dataset; and apply computer aided detection (CAD) to thecanonically mapped volume to detect a region of disease of the organ.